The new histological system for the diagnosis of adrenocortical cancer.

Introduction: Adrenocortical cancer (ACC) is a rare malignant tumor that originates in the adrenal cortex. Despite extensive molecular-genetic, pathomorphological, and clinical research, assessing the malignant potential of adrenal neoplasms in clinical practice remains a daunting task in histological diagnosis. Although the Weiss score is the most prevalent method for diagnosing ACC, its limitations necessitate additional algorithms for specific histological variants. Unequal diagnostic value, subjectivity in evaluation, and interpretation challenges contribute to a gray zone where the reliable assessment of a tumor's malignant potential is unattainable. In this study, we introduce a universal mathematical model for the differential diagnosis of all morphological types of ACC in adults. Methods: This model was developed by analyzing a retrospective sample of data from 143 patients who underwent histological and immunohistochemical examinations of surgically removed adrenal neoplasms. Statistical analysis was carried out on Python 3.1 in the Google Colab environment. The cutting point was chosen according to Youden's index. Scikit-learn 1.0.2 was used for building the multidimensional model for Python. Logistical regression analysis was executed with L1-regularization, which is an effective method for extracting the most significant features of the model. Results: The new system we have developed is a diagnostically meaningful set of indicators that takes into account a smaller number of criteria from the currently used Weiss scale. To validate the obtained model, we divided the initial sample set into training and test sets in a 9:1 ratio, respectively. The diagnostic algorithm is highly accurate [overall accuracy 100% (95% CI: 96%-100%)]. Discussion: Our method involves determining eight diagnostically significant indicators that enable the calculation of ACC development probability using specified formulas. This approach may potentially enhance diagnostic precision and facilitate improved clinical outcomes in ACC management.

Health and nutrition claims for infant formula: international cross sectional survey.

OBJECTIVES: To review available health and nutrition claims for infant formula products in multiple countries and to evaluate the validity of the evidence used for substantiation of claims. DESIGN: International cross sectional survey. SETTING: Public facing and healthcare professional facing company owned or company managed formula industry websites providing information about products marketed for healthy infants delivered at full term in 15 countries: Australia, Canada, Germany, India, Italy, Japan, Nigeria, Norway, Pakistan, Russia, Saudi Arabia, South Africa, Spain, the United Kingdom, and the United States in 2020-22. MAIN OUTCOME MEASURES: Number and type of claims made for each product and ingredient. References cited were reviewed and risk of bias was assessed for registered clinical trials using the Cochrane risk of bias tool, and for systematic reviews using the Risk Of Bias in Systematic reviews tool. RESULTS: 757 infant formula products were identified, each with a median of two claims (range from 1 (Australia) to 4 (US)), and 31 types of claims across all products. Of 608 products with ≥1 claims, the most common claim types were "helps/supports development of brain and/or eyes and/or nervous system" (323 (53%) products, 13 ingredients), "strengthens/supports a healthy immune system" (239 (39%) products, 12 ingredients), and "helps/supports growth and development" (224 (37%) products, 20 ingredients). 41 groups of ingredients were associated with ≥1claims, but many claims were made without reference to a specific ingredient (307 (50%) products). The most common groups of ingredients cited in claims were long chain polyunsaturated fatty acids (278 (46%) products, 9 different claims); prebiotics, probiotics, or synbiotics (225 (37%) products, 19 claims); and hydrolysed protein (120 (20%) products, 9 claims). 161/608 (26%) products with ≥1 claims provided a scientific reference to support the claim-266 unique references were cited for 24 different claim types for 161 products. The reference types most frequently cited were clinical trials (50%, 134/266) and reviews (20%, 52/266). 28% (38/134) of referenced clinical trials were registered, 14% (19/134) prospectively. 58 claims referred to 32 registered clinical trials, of which 51 claims (27 trials) related to a randomised comparison. 46 of 51 claims (90%) referenced registered clinical trial outcomes at high risk of bias, and all cited systematic reviews and pooled analyses, carried a high risk of bias. CONCLUSIONS: Most infant formula products had at least one health and nutrition claim. Multiple ingredients were claimed to achieve similar health or nutrition effects, multiple claims were made for the same ingredient type, most products did not provide scientific references to support claims, and referenced claims were not supported by robust clinical trial evidence.